[R] Re : Running random forest using different training and testing schemes
Hi Chysanthi, check out the randomForest package, with the function randomForest. It has a CV option. Sorry for not providing you with a lengthier response at the moment but I'm rather busy on a project. Let me know if you need more help. Also, to split your data into two parts- the training and the test set you can do (n the number of data points): n-length(data[,1]) indices-sample(rep(c(TRUE,FALSE),each=n/2),round(n/2),replace=TRUE) training_indices-(1:n)[indices] test_indices-(1:n)[!indices] Then, data[train,] is the training set and data[test,] is the test set. Best, Pierre De : Chrysanthi A. chrys...@gmail.com À : r-help@r-project.org Envoyé le : Dimanche, 12 Avril 2009, 17h26mn 59s Objet : [R] Running random forest using different training and testing schemes Hi, I would like to run random Forest classification algorithm and check the accuracy of the prediction according to different training and testing schemes. For example, extracting 70% of the samples for training and the rest for testing, or using 10-fold cross validation scheme. How can I do that? Is there a function? Thanks a lot, Chrysanthi. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Re : Running random forest using different training and testing schemes
you need to include in your code something like: tree-rpart(result~., data, control=rpart.control(xval=10)). this xval=10 is 10-fold CV. Best, Pierre De : Chrysanthi A. chrys...@gmail.com À : r-help@r-project.org Envoyé le : Dimanche, 12 Avril 2009, 17h26mn 59s Objet : [R] Running random forest using different training and testing schemes Hi, I would like to run random Forest classification algorithm and check the accuracy of the prediction according to different training and testing schemes. For example, extracting 70% of the samples for training and the rest for testing, or using 10-fold cross validation scheme. How can I do that? Is there a function? Thanks a lot, Chrysanthi. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Re : Running random forest using different training and testing schemes
Hi Pierre, Thanks a lot for your help.. So, using that script, I just separate my data in two parts, right? For using as training set the 70 % of the data and the rest as test, should I multiply the n with the 0.70 (for this case)? Many thanks, Chrysanthi 2009/4/12 Pierre Moffard pier.m...@yahoo.fr Hi Chysanthi, check out the randomForest package, with the function randomForest. It has a CV option. Sorry for not providing you with a lengthier response at the moment but I'm rather busy on a project. Let me know if you need more help. Also, to split your data into two parts- the training and the test set you can do (n the number of data points): n-length(data[,1]) indices-sample(rep(c(TRUE,FALSE),each=n/2),round(n/2),replace=TRUE) training_indices-(1:n)[indices] test_indices-(1:n)[!indices] Then, data[train,] is the training set and data[test,] is the test set. Best, Pierre -- *De :* Chrysanthi A. chrys...@gmail.com *À :* r-h...@r-project..org *Envoyé le :* Dimanche, 12 Avril 2009, 17h26mn 59s *Objet :* [R] Running random forest using different training and testing schemes Hi, I would like to run random Forest classification algorithm and check the accuracy of the prediction according to different training and testing schemes. For example, extracting 70% of the samples for training and the rest for testing, or using 10-fold cross validation scheme. How can I do that? Is there a function? Thanks a lot, Chrysanthi. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
